sim_weights: Simulate Normalized Weights

In brisk: Bayesian Benefit Risk Analysis

Simulate Normalized Weights

Description

Simulate Normalized Weights

Usage

sim_weights(n, ...)

Arguments

n	number of weights to simulate.
...	vectors of length 2 indicating the lower and upper bound (respectively) of the un-normalized weights. At least one set of bounds must be equal to each other (e.g. c(1, 1)) and be the largest set of bounds in the set specified.

Details

The weights are normalized relative to a set of bounds which are equal to each other (e.g. c(1, 1)), and also are the largest set of bounds in the set specified. See Example.

Value

A tibble with weights for each argument supplied to .... Each column represents the weights, and each row (total of n rows) is a set of random weights across groups. Column names are obtained from the argument names of ..., if supplied.

Examples

w <- sim_weights(1e4, a = c(1, 1), b = c(.4, .6), c = c(.2, .3))
# ratio of b to a is between c(.4, .6) / c(1, 1)
summary(w$b / w$a)
# ratio of c to a is between c(.2, .3) / c(1, 1)
summary(w$c / w$a)

# Weights can be used to add uncertainty to the benefit/risk analysis
set.seed(1132)
ilogit <- function(x) 1 / (1 + exp(-x))
out <- mcda(
  benefit("CV", function(x) ilogit(x), weight = w$a),
  risk("DVT", function(x) ilogit(- .5 * x), weight = w$b),
  risk("MI", function(x) ilogit(- .5 * x), weight = w$c),
  br_group(
    label = "PBO",
    CV = rnorm(1e4, .1),
    DVT = rnorm(1e4, .1),
    MI = rnorm(1e4, .1)
  ),
  br_group(
    label = "TRT",
    CV = rnorm(1e4, 2),
    DVT = rnorm(1e4, 1),
    MI = rnorm(1e4, 0.5)
  )
)

brisk documentation built on Aug. 31, 2022, 9:15 a.m.